YeastHub: a semantic web use case for integrating data in the life sciences domain

Area of Science:

• Bioinformatics
• Computational Biology
• Semantic Web Technologies

Background:

• Growing need for life sciences data integration over the web.
• Lack of widely-accepted standards for data syntax and semantics.
• Maturing semantic web technology offers potential solutions.

Purpose of the Study:

• Explore semantic web technologies for life sciences data integration.
• Address the challenge of data heterogeneity and lack of standards.
• Develop a prototype for integrated biological data querying.

Main Methods:

• Utilized Resource Description Framework (RDF) and related technologies.
• Developed a relational-database-to-RDF mapping (D2RQ) approach.
• Implemented a native RDF data repository (Sesame) for data storage and querying.

Main Results:

• Introduced a novel RDF structure for converting tabular biological datasets.
• Developed YeastHub, a web-based application demonstrating a life sciences data warehouse.
• Successfully integrated diverse yeast genome data from multiple sources and formats.

Conclusions:

• Semantic web technologies can effectively address life sciences data integration challenges.
• The YeastHub prototype showcases the feasibility of RDF-based data warehousing.
• Integrated querying of heterogeneous biological data is achievable using RDF and native data stores.